176,699 research outputs found
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
Testing in Continuous Integration (CI) involves test case prioritization,
selection, and execution at each cycle. Selecting the most promising test cases
to detect bugs is hard if there are uncertainties on the impact of committed
code changes or, if traceability links between code and tests are not
available. This paper introduces Retecs, a new method for automatically
learning test case selection and prioritization in CI with the goal to minimize
the round-trip time between code commits and developer feedback on failed test
cases. The Retecs method uses reinforcement learning to select and prioritize
test cases according to their duration, previous last execution and failure
history. In a constantly changing environment, where new test cases are created
and obsolete test cases are deleted, the Retecs method learns to prioritize
error-prone test cases higher under guidance of a reward function and by
observing previous CI cycles. By applying Retecs on data extracted from three
industrial case studies, we show for the first time that reinforcement learning
enables fruitful automatic adaptive test case selection and prioritization in
CI and regression testing.Comment: Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017).
Reinforcement Learning for Automatic Test Case Prioritization and Selection
in Continuous Integration. In Proceedings of 26th International Symposium on
Software Testing and Analysis (ISSTA'17) (pp. 12--22). AC
Structural reliability prediction of a steel bridge element using dynamic object oriented Bayesian Network (DOOBN)
Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method
Overview of methods to analyse dynamic data
This book gives an overview of existing data analysis methods to analyse the dynamic data obtained from full scale testing, with their advantages and drawbacks. The overview of full scale testing and dynamic data analysis is limited to energy performance characterization of either building components or whole buildings.
The methods range from averaging and regression methods to dynamic approaches based on system identification techniques. These methods are discussed in relation to their application in following in situ measurements:
-measurement of thermal transmittance of building components based on heat flux meters;
-measurement of thermal and solar transmittance of building components tested in outdoor calorimetric test cells;
-measurement of heat transfer coefficient and solar aperture of whole buildings based on co-heating or transient heating tests;
-characterisation of the energy performance of whole buildings based on energy use monitoring
Tone from the Top in Risk Management: A Complementarity Perspective on How Control Systems Influence Risk Awareness
Prompted by the weaknesses of standardized risk management approaches in the aftermath of the
2008 financial crisis, scholars, regulators, and practitioners alike emphasize the importance of
creating a risk-aware culture in organizations. Recent insights highlight the special role of tone
from the top as crucial driver of risk awareness. In this study, we take a systems-perspective on
control system design to investigate the role of tone from the top in creating risk awareness. In
particular, we argue that both interactive and diagnostic use of budgets and performance measures
interact with tone from the top in managing risk awareness. Our results show that interactive control
strengthens the effect of tone from the top on risk awareness, while tone from the top and diagnostic
control are, on average, not interrelated with regard to creating risk awareness. To shed light on the
boundary conditions of the proposed interdependencies, we further investigate whether the
predicted interdependencies are sensitive to the level of perceived environmental uncertainty. We
find that the effect of tone from the top and interactive control becomes significantly stronger in a
situation of high perceived environmental uncertainty. Most interestingly, tone from the top and
diagnostic control are complements with regard to risk awareness in settings of low perceived
environmental uncertainty and substitutes at high levels of perceived environmental uncertainty.Series: Department of Strategy and Innovation Working Paper Serie
Reliability-Informed Beat Tracking of Musical Signals
Abstract—A new probabilistic framework for beat tracking of musical audio is presented. The method estimates the time between consecutive beat events and exploits both beat and non-beat information by explicitly modeling non-beat states. In addition to the beat times, a measure of the expected accuracy of the estimated beats is provided. The quality of the observations used for beat tracking is measured and the reliability of the beats is automatically calculated. A k-nearest neighbor regression algorithm is proposed to predict the accuracy of the beat estimates. The performance of the beat tracking system is statistically evaluated using a database of 222 musical signals of various genres. We show that modeling non-beat states leads to a significant increase in performance. In addition, a large experiment where the parameters of the model are automatically learned has been completed. Results show that simple approximations for the parameters of the model can be used. Furthermore, the performance of the system is compared with existing algorithms. Finally, a new perspective for beat tracking evaluation is presented. We show how reliability information can be successfully used to increase the mean performance of the proposed algorithm and discuss how far automatic beat tracking is from human tapping. Index Terms—Beat-tracking, beat quality, beat-tracking reliability, k-nearest neighbor (k-NN) regression, music signal processing. I
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
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